MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery

Abstract

Accurate flood delineation is crucial in many disaster management tasks, such as risk map production and update, impact estimation, claim verification, or planning of countermeasures for disaster risk reduction. Open remote sensing resources such as the data provided by the Copernicus ecosystem enable to carry out this activity, which benefits from frequent revisit times on a global scale. In the last decades, satellite imagery has been successfully applied to flood delineation problems, especially considering Synthetic Aperture Radar (SAR) signals. However, current remote mapping services rely on time-consuming manual or semi-automated approaches, requiring the intervention of domain experts. The implementation of accurate and scalable automated pipelines is hindered by the scarcity of large-scale annotated datasets. To address these issues, we propose MMFlood, a multimodal remote sensing dataset purposely designed for flood delineation. The dataset contains 1,748 Sentinel-1 acquisitions, comprising 95 flood events distributed across 42 countries. Along with satellite imagery, the dataset includes the Digital Elevation Model (DEM), hydrography maps, and flood delineation maps provided by Copernicus EMS, which is considered as ground truth. To provide baseline performances on the MMFlood test set, we conduct a number of experiments of the flood delineation task using state-of-art deep learning models, and we evaluate the performance gains of entropy-based sampling and multi-encoder architectures, which are respectively used to tackle two of the main challenges posed by MMFlood, namely the class unbalance and the multimodal setting. Lastly, we provide a future outlook on how to further improve the performance of the flood delineation task. Dataset and code can be found at https://github.com/edornd/mmflood.

Keywords

• Computer vision
• deep learning
• image processing
• machine learning
• semantic segmentation
• remote sensing